Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when building the reproducibility story for an ACM CoNEXT paper — pinned traces and configs, a runnable artifact, an honest data-availability posture, and the one-page…
Install Conext Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills conext-reproducibility --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/CoNEXT-Skills/skills/conext-reproducibility .claude/skills/conext-reproducibility && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "conext-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoNEXT-Skills/skills/conext-reproducibility into .claude/skills/conext-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conext-reproducibility", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoNEXT-Skills/skills/conext-reproducibilityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills conext-reproducibility --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/CoNEXT-Skills/skills/conext-reproducibility .agents/skills/conext-reproducibility && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "conext-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoNEXT-Skills/skills/conext-reproducibility into .agents/skills/conext-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conext-reproducibility", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills conext-reproducibility --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/CoNEXT-Skills/skills/conext-reproducibility .cursor/skills/conext-reproducibility && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "conext-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoNEXT-Skills/skills/conext-reproducibility into .cursor/skills/conext-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conext-reproducibility", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path CoNEXT-Skills/skills/conext-reproducibility--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills conext-reproducibility --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/CoNEXT-Skills/skills/conext-reproducibility .gemini/skills/conext-reproducibility && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "conext-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoNEXT-Skills/skills/conext-reproducibility into .gemini/skills/conext-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conext-reproducibility", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills conext-reproducibilityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-reproducibility -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/CoNEXT-Skills/skills/conext-reproducibility .github/skills/conext-reproducibility && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "conext-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoNEXT-Skills/skills/conext-reproducibility into .github/skills/conext-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conext-reproducibility", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-reproducibility -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills conext-reproducibility --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/CoNEXT-Skills/skills/conext-reproducibility .opencode/skills/conext-reproducibility && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "conext-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoNEXT-Skills/skills/conext-reproducibility into .opencode/skills/conext-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "conext-reproducibility", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
conext-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 444 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when building the reproducibility story for an ACM CoNEXT paper — pinned traces and configs, a runnable artifact, an honest data-availability posture, and the one-page…
- Building the reproducibility story for an ACM CoNEXT paper — pinned traces and configs
- SKILL.md covers The timing trap (read this…, What "reproducible" means for…, Pin provenance at collection… and Honest data-availability posture, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- A runnable artifact
What it does
Conext Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the reproducibility story for an ACM CoNEXT paper — pinned traces and configs, a runnable artifact, an honest data-availability posture, and the one-page artifact description the CoNEXT reproducibility committee needs — remembering that the ACM badge opt-in is due before the submission deadline.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Reproducible research. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Building the reproducibility story for an ACM CoNEXT paper — pinned traces and configs
- A runnable artifact
- An honest data-availability posture
Example prompts
- “/conext-reproducibility”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Conext Reproducibility loads about 1.3k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 444 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 444 words, ~1,289 tokens.
Download SKILL.mdSave it as
.claude/skills/conext-reproducibility/SKILL.md (or your agent's skills folder).- name
- conext-reproducibility
- description
- Use when building the reproducibility story for an ACM CoNEXT paper — pinned traces and configs, a runnable artifact, an honest data-availability posture, and the one-page artifact description the CoNEXT reproducibility committee needs — remembering that the ACM badge opt-in is due before the submission deadline.
CoNEXT Reproducibility
Build the reproducible-networking story alongside the experiments, not after acceptance. CoNEXT runs a dedicated reproducibility committee that awards optional ACM badges, and the single most missed rule is that badge eligibility requires opting in before the paper submission deadline — you cannot bolt it on later. Even for authors who skip badging, a reproducible artifact strengthens a double-anonymous, one-shot-revision review.
The timing trap (read this first)
[Before the submission deadline] OPT IN for ACM badging (required for eligibility)
[At submission] anonymized, runnable artifact referenced from the paper
[Within ~1 week of acceptance] send a ONE-PAGE artifact description to the reproducibility
committee with pointers to code and other artifacts
[Camera-ready / post-accept] committee evaluates for Available / Functional / Reusable /
Reproduced badges (see conext-artifact-evaluation)Miss the opt-in and the strongest artifact in the world cannot earn a badge this cycle.
What "reproducible" means for a networking paper
Networking reproducibility is harder than "here is the code," because the result depends on an environment:
- Traces: the exact captures, with vantage points, dates, and anonymization documented, plus the extraction scripts that turn raw captures into the paper's inputs.
- Configs: the exact parameters, topology, and settings used for each figure — not a representative example.
- Environment: hardware models, firmware, kernel/OS versions, link rates, and buffer depths for a testbed; container/VM images where feasible.
- Pipeline: a scripted path from raw data/trace to each table and figure, so the numbers regenerate rather than being hand-copied.
Pin provenance at collection time
You cannot reconstruct provenance after the campaign ends:
- Record capture vantage points, timestamps, and anonymization method as you collect.
- Snapshot configs and firmware/OS versions at run time; a later "we think it was v2.1" is not reproducible.
- For ML-for-networking, pin model identifiers and dates and cache raw model outputs — a package that needs live API calls re-samples rather than reproduces.
Show full SKILL.md (197 more words)Show less
Honest data-availability posture
- If you can release traces/code, do — with a DOI-issuing archive (Zenodo/figshare/Software Heritage) and an open license.
- If operator agreements or privacy limits block release, say so and why, and release what you can (aggregate data, synthetic traces, the analysis pipeline). "Available upon request" reads as a scored weakness, not a neutral placeholder.
- Keep the availability statement honest and matched to what the artifact actually contains — a reviewer or the committee will check.
Anonymity of the artifact (double-anonymous review)
- Re-host the artifact behind an anonymizing service before submission; scrub commit metadata, internal hostnames, and owner-identifying paths.
- Topology diagrams and configs can leak an operator or institution — sanitize AS numbers, hostnames, and IP ranges you own.
- The reproducibility material referenced at review time must not de-anonymize you.
The one-page artifact description
After acceptance, the committee wants a one-page description that lets an evaluator start quickly:
- What the artifact contains (code, traces, configs, testbed scripts) and pointers to each.
- The hardware/software the evaluator needs, and any hardware you must provide access to (some networking artifacts need specific switches/NICs — flag this early).
- A short "getting started" path and the claims the artifact supports.
Pre-submission reproducibility audit
[Opt-in] badge opt-in done BEFORE the submission deadline? (if badging) yes/no
[Traces] captures + vantage points + dates + extraction scripts present? yes/no
[Configs] exact per-figure configs and topology recorded? yes/no
[Environment] hardware/firmware/OS versions pinned; image where feasible? yes/no
[Pipeline] raw -> figure regenerates by script? yes/no
[Availability] honest statement; DOI archive or a documented reason not to release? yes/no
[Anonymity] artifact re-hosted anonymously; metadata scrubbed? yes/noOutput format
[Reproducibility status] ready / gaps
[Badge intent] opt-in before submission? yes/no/n-a
[Provenance] traces/configs/environment pinned at collection time
[Availability] what is released, where (DOI), and any documented restriction
[Anonymity] artifact anonymized for double-anonymous review
[One-pager] artifact description drafted for the committee (post-accept)© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in CoNEXT-Skills/skills/conext-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Conext Reproducibility next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Conext Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
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CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
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Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Categories
Questions about Conext Reproducibility
What does Conext Reproducibility do?
A skill your agent uses when building the reproducibility story for an ACM CoNEXT paper — pinned traces and configs, a runnable artifact, an honest data-availability posture, and the one-page…. Conext Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the reproducibility story for an ACM CoNEXT paper — pinned traces and configs, a runnable artifact, an honest data-availability posture, and the one-page artifact description the CoNEXT reproducibility committee needs — remembering that the ACM badge opt-in is due before the submission deadline.
When should I use Conext Reproducibility?
Conext Reproducibility fits situations like: building the reproducibility story for an ACM CoNEXT paper — pinned traces and configs; A runnable artifact; an honest data-availability posture.
How do I install Conext Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-reproducibility -a claude-code`. Or copy the skill folder (CoNEXT-Skills/skills/conext-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/conext-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Conext Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-reproducibility -a codex`. Or copy the skill folder (CoNEXT-Skills/skills/conext-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/conext-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Conext Reproducibility in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-reproducibility -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/conext-reproducibility, .gemini/skills/conext-reproducibility, .github/skills/conext-reproducibility and .opencode/skills/conext-reproducibility in your project.
What does Conext Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Conext Reproducibility is instructions for the agent only.
Does Conext Reproducibility access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Conext Reproducibility safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Conext Reproducibility use?
Conext Reproducibility is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Conext Reproducibility use?
About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Conext Reproducibility?
Skills that share tags, products or a category with Conext Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Conext Reproducibility?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.